The Pharmaceutical industry is really good at employing molecular-level chemistry models to help predict likely new cures for diseases. On the flip side it is bad (vs. other industries) at applying modelling to manufacturing to predict how and when to make products most effectively. This is for a number of reasons, one of which is the lack of good quality data. This would enable the models to make better predictions; as more good data, leads to better predictions. One way to get more data is to make it yourself, but that's expensive and wasteful as a solo effort. It's much better if you can share. Companies find it hard to trust each other sharing data though, as they are competitors. So sharing is blocked by cyber security concerns, commercial threats, and the lack of certainty that the data will be used as intended. One way to fix these concerns is to employ Federated Data sharing technologies. These novel digital tools address the concerns of 'who has access to data' and 'why', because you can control these aspects centrally. They are also very cyber secure. They do not solve the concerns of commercial threat, however. As, if you share all of your data, you may well give away valuable secrets. The obvious solution is to share data (through the new technologies) but share segments of the data, not the whole. This way modelling outcomes can be achieved more effectively, but you're not giving away valuable information. The trouble here is that there is little evidence that redacted datasets lead to better modelling outcomes. There is also a business risk, as there are very few practical tools available to determine how much data is 'too much' data shared. This project (SHARPEN) intends to deliver a platform for data sharing (so we can assess it) that runs across R&D data to manufacturing (ensuring good data transfer across all relevant data) and deliver a risk assessment tool (to enable rapid assessment and subsequent sharing of data), as well as working out what someone would pay for that service. We will deliver the outcomes through diverse partners who have significant experience in the pharmaceutical sector and who've successfully worked together in the past. We will enable a number of market ready digital tools in the process. Ensuring medicines manufacturing becomes more efficient through effective use of models to accurately predict what to do next.

In order to meet National and International medicines demand, both small and large pharmaceutical companies need to use multiple suppliers. It takes significant effort to coordinate these separate companies and create an integrated plan that safely ensures patients get the medicines they need. Any changes to the plan take time and effort to manage and this creates waste. To add to the complexity, in order to sign off a batch of medicines, a Qualified Person ( QP, the person who assures patient safety for a given company) must see all of this data in order legally to release the medicines into the supply chain. The challenge of assembling this data can add delay. As the requirements of patients change, with an older population along with other constraints such as increasing complexity of medicines, more of the supply chain will migrate towards mixed-company models, putting stress on pricing and the ability of companies to deliver on their promises. The industry can't just 'throw more people at it' forever to solve the problem. Data science can enable people to streamline the movement of information and look at these problems differently. Once the passage of data has been solved it will even be possible to migrate some of the decisions to electronic systems by using Machine Learning (ML) and Artificial Intelligence (AI) solutions. This would allow QPs to focus on important questions. SmartPSC aims to apply these technologies to reduce waste, increase speed and improve access to medicines. Creating a template by which this can robustly be done will be complex, as it will mean connecting ways of working and systems that have not standardly been connected as well as overcoming data security challenges. This project aims to create the foundation for delivering this integrated supply chain vision by leveraging expertise and capabilities across a group of small and large companies within the UK pharma supply chain (SC) to deliver two work packages: 1\. Designing and implementing a way to get everyone to share their information in a quickly usable format, so it can move seamlessly and securely between companies so that we can build a real-time multi-company view of the supply chain. 2\. Link data from multiple companies so a QP can quickly and safely make decisions. By delivering on this scope, we will be able to template future supply chains.